System and method for blindly acquiring frequency hopped spread spectrum signals

ABSTRACT

A system and method for acquiring a frequency hopped spread spectrum (FHSS) signal with no prior knowledge about the FHSS signal. In example implementations, an RF signal is received at a receiver. The RF signal is converted into a stream of digital signal levels. Energy detections are identified in the stream of digital signal as possible hops of a FHSS signal. A feature set is blindly acquired for defining an FHSS signal from the energy detections. At least one waveform classification is generated based on the feature set. Energy detections are re-acquired from the RF signal based on the waveform classification.

BACKGROUND 1. Field

The present disclosure relates generally to radio signal communicationsystems, and more particularly, to systems and methods for acquiringsignals modulated according to a frequency hopped spread spectrum schemewithout prior knowledge of any details about the signal.

2. Related Art

Frequency hopped signals are radio signals transmitted between atransmitter and receiver that periodically switches the carrier amongdifferent frequency channels. The Frequency Hopped Spread Spectrum(FHSS) modulation scheme uses a specific sequence known to both thetransmitter and receiver to determine a sequence of frequencies on whichthe signal transmits. In general, the FHSS communication “spreads” itssignal over a number of rapidly changing frequencies by “hopping” amongthe different frequencies in the shared sequence. Frequency hoppingtechniques are utilized in a variety of applications. The mostwell-known use of the FHSS technique is in Push-to-Talk radios, radiocontrolled drones, and long range radios. Frequency hopping is alsoutilized in agile radar systems in which energy is transmitted with theexpectation of receiving a reflection echo from an object of interest.The energy is transmitted as energy bursts at different frequencies.

Frequency hopping provides a number of advantages over fixed-frequencytransmission schemes that include resistance to noise interference,security, and shared frequency band usage. With regard to security, amain advantage of utilizing frequency hopping techniques is thatfrequency hopping provides a low probability of intercept. Transmittersusing frequency hopping switch or hop from one frequency to another in apre-determined sequence. In order for a receiver to properly receive andde-modulate the frequency hopped signal, the receiver must first knowthe pre-determined hop sequence. Typically, after initiating a frequencyhopped connection, the transmitter and receiver may share informationthat allows each to know any updated hop sequence information so as toallow for continued communication.

Due to the low probability of intercept, frequency hopping techniquesare often utilized in military or secure commercial applications.Unfortunately, at present, frequency hopping techniques are also used bycriminal enterprises and in other scenarios by people or organizationsthat would be of interest to law enforcement or public safety officialsin general. In such situations, there is a need to intercept thefrequency hopped signals of these enterprises, people, or organizationsin the interest of public safety. Since in these situations the receiverwill not have access to the pre-determined sequence of a targettransmitter, there is a need to acquire target frequency hopped signalswithout having prior knowledge about the details of the target signal.

BRIEF DESCRIPTION OF THE FIGURES

This disclosure may be better understood by referring to the followingfigures. The components in the figures are not necessarily to scale,emphasis instead being placed upon illustrating the principles of thedisclosure. In the figures, like reference numerals designatecorresponding parts throughout the different views.

FIG. 1A is a block diagram of an example of an implementation of asystem for acquiring a frequency hopped signal without prior knowledgeof the details of the signal in accordance with the present disclosure.

FIG. 1B is a block diagram illustrating the utilization of a maximumlikelihood classifier in acquiring feature sets and waveformclassifications.

FIG. 1C is a flowchart illustrating the operation of an example of animplementation of method for acquiring a frequency hopped signal withoutprior knowledge about the signal in accordance with the presentdisclosure.

FIG. 2 is a block diagram of an example of an implementation of a blindfeature acquisition and waveform classification system that is utilizedas an example system for blindly acquiring an FHSS signal in accordancewith the present disclosure.

FIGS. 3A and 3B are block diagrams of an example of an implementation ofweak classifiers for blindly acquiring features from energy detectionsin accordance with the present disclosure.

FIG. 4 is a schematic diagram illustrating an example of animplementation of a hopping scheme in which shape features are blindlyacquired in accordance with the present disclosure.

FIG. 5 is a schematic diagram illustrating an example of animplementation of a hopping scheme in which frequency features areblindly acquired in accordance with the present disclosure.

FIG. 6 is a schematic diagram illustrating an example of animplementation of a hopping scheme in which time features are blindlyacquired in accordance with the present disclosure.

FIG. 7 is a schematic diagram illustrating an example of animplementation of a hopping scheme in which directional features areblindly acquired in accordance with the present disclosure.

FIG. 8A is a screenshot of an example of a report for a blindacquisition of a frequency hopped signal (FHSS).

FIG. 8B is a screenshot of an example of a report of a completed FHSSwaveform detection.

DETAILED DESCRIPTION

In this disclosure, systems and methods are described for acquiringfrequency hopped signals from received RF signals without priorknowledge of the details of the frequency hopped signals. In an exampleof an implementation, the systems and methods described within thisdisclosure may be performed by an analytical instrument configured toblindly acquire the frequency hopped signals and to generate reportscontaining details about the frequency hopped signals. In general, thereports with details about the frequency hopped signals may be displayedon a display device integrated with the instrument, printed on anattached printing device, or communicated over a data network to aremote network-connected user. Additionally, in other implementations,the systems and methods may be performed by systems having other signalanalysis functions of which a system for acquiring frequency hoppedsignals is one of the function. Moreover, in other implementations, thesystems and methods for acquiring frequency hopped signals may be a partof a signal jamming device where the details of the acquired frequencyhopped signals may be utilized to generate jamming signals.

As utilized in this description, the term “component” shall mean eithera hardware device, a software function, or program, or a function orprogram performed utilizing both a software program that interfaces witha hardware device.

FIG. 1A is a block diagram of an example of an implementation of asystem 100 for acquiring a Frequency Hopped Spread Spectrum (FHSS)signal without prior knowledge of the details of the signal inaccordance with the present disclosure. The system 100 includes a radiofrequency (RF) receiver 102, a front-end processor 106 (which may be asignal processor), a digitizer 107, an energy detector 108, and a signalidentification system 120. The RF receiver 102, digitizer 107, energydetector 108, and signal identification system 120 include knownhardware and software components for receiving RF signals, converting ananalog RF signal to a digital signal stream, and to identify energydetections in the digital signal stream. In the example shown in FIG.1A, the front-end processor 106 is included to perform digital signalprocessing functions for either the RF receiver 102, the digitizer 107,the energy detector 108, or for the signal identification system 120.

The signal identification system 120 includes both an autonomous anddirected re-acquisition system. The autonomous system performs anunsupervised machine learning method that utilizes a maximum likelihoodclassifier and ensemble learning process that utilizes individualfeature acquisition and track algorithms to generate waveform details orclassifications that correspond to one or more frequency hopped signals.In this example, the directed re-acquisition system may receive full orpartial a priori information from the autonomous system or from a userto add to the details of the waveform classifications. In an example ofoperation, the signal identification system 120 receives energydetections from energy detector 108 or portions of a digitized RF signalhaving signal levels above a predetermined threshold over the noisefloor estimate. The energy detections are analyzed to generate featuresabout the energy detections, which may be correlated to classify andidentify the frequency hopped signal.

The RF receiver 102 is configured to receive RF signals from one or moreantennas 104, which are in signal communication with the receiver 102.The RF receiver 102 employs the front-end processor 106 to process thereceived RF signals, from the RF receiver 102, to generate a digitalsignal stream from the received RF signals. The digital signal stream isinput into the energy detector 108 and the energy detector 108determines a noise floor estimate for the digital signal stream. Theenergy detector 108 then identifies, as energy detections, portions ofthe digital signal stream with levels above the noise floor estimate. Itis appreciated by those of ordinary skill in the art that little isknown about the energy detections at this point, except that it ispossible that the energy detections are not noise. As such, the energydetections represent portions of a signal that may be determined to be a“blindly” (i.e., without prior knowledge of the details of the signal)acquired frequency hopped signal.

The energy detector 108 may utilize known signal processing techniquesto identify the energy detections. For example, in an example of animplementation, the energy detector 108 may process the in-phase andquadrature phase (I and Q) digital signal stream using fast Fouriertransform analysis over a period of time. The energy detector 108 mayperform, for example, 1000 FFTs/sec., or any other suitable number ofFFTs/sec., to detect signals at certain frequencies and in certain timeslots where such signals have energy levels above the predeterminednoise floor. The energy detections may then be identified by themeasured signal power, the turn-on and turn-off times in the one secondof measurements, the bandwidth, and the center frequency of the signal.The signal identification system 120 may determine features from theacquired energy detections and then determine the likelihood that thefeatures belong to one or more waveforms forming one or more frequencyhopped signals.

The energy detections are provided as input to the signal identificationsystem 120, which includes a blind acquisition component 122, a directedreacquisition component 126, and a full ensemble waveform classifier124. The blind acquisition component 122, the full ensemble waveformclassifier 124, and the directed re-acquisition component 126 usemaximum likelihood classifiers and derived statistics to correlate theenergy detections that belong to the same frequency hopped signal basedon correlated features.

The frequency hopped system 100 includes a processor 130, anon-transitory computer storage medium (i.e., memory) 132, and a userinterface (I/F) 134. The example illustrated in FIG. 1A employs thefront-end processor 106 for signal processing functions, and a secondprocessor 130 (such as, for example, a general purpose processor) forexecuting software programs that perform functions not directlyrequiring digital signal processing resources. The software programs mayinvoke functions that utilize digital signal processing, such as FFTfunctions, digital filtering functions, or other signal processingfunctions. In another example of an implementation, the system 100 mayoperate using a single processor to the extent that the processor iscapable of performing signal processing and general processingfunctions. The memory 132 may be utilized for storing computer readableinstructions (such as, for example, software) that when executed by theprocessor 130 perform the functions required by the blind acquisitioncomponent 122, the full ensemble waveform classifier 124, and thedirected re-acquisition component 126, as well as other functions orcomponents described with reference to FIGS. 1B and 2-8.

The user interface 134 may provide drivers and other software andhardware for communicating with user interface devices, such as adisplay device (i.e., display) 136, a printer, a keyboard, a mouse orother pointing type devices, and any other suitable input/output (I/O)device. It is noted that only a display 136 is shown in FIG. 1A;however, user interface devices are well known in the art.

The blind acquisition component 122 is a module, program, sub-system,device, etc. that is configured to perform as multiple independentunsupervised machine learning classification processes where thephysical features of the energy detections are the random variables(i.e., features) that need to be observed and learned. Each feature hasan unknown probability density function (PDF) and an unknown dependencewith the other variables. In addition, a feature may or may not beactive and correlated for a particular waveform. The process determinesa potential waveform by learning active correlated features in thepresence of noise and other independent waveforms.

The blind acquisition component 122 measures waveform characteristicsused to determine the features of the energy detections. In thisdisclosure, the term “feature” shall refer to a parameter or measuredsignal characteristic that may be used in combination with otherfeatures to classify a waveform, or a frequency hopped signal inparticular. The features are treated as random variables characterizinga signal about which no information is available. In an example of animplementation, a feature set may include features such as detect power,on-time period or phase, off-time period or phase, detect frequency andbandwidth, direction of arrival, waveform frequency use and bandwidth,waveform burst duration, channel width, and channel spacing. Thewaveform characteristics are measured in the energy detections toacquire the features of the energy detections. In this example,statistics, such as a PDF, may also be determined for each feature.

Once the features are determined from the energy detections, a featureset is derived from these features by correlating the features using amaximum likelihood classifier. In an example of an implementation, theblind acquisition component 122 includes a blind signal characteristicdetector to detect signal characteristics as feature, and a weak maximumlikelihood classifier that incorporates Kalman filtering to receiveenergy detections and to determine the likelihood the energy detectionscorresponding to a feature set for a potential waveform. The featuresthen may be stored as feature sets 160 in a system database 125.

The full ensemble waveform classifier 124 performs a maximum likelihoodclassification operation to determine if a potential waveform identifiedby the blind acquisition component 122 belongs to a known waveformclassification. The logic followed by the full ensemble waveformclassifier 124 may be the same or similar to that followed by the blindacquisition component 122 as both employ a maximum likelihoodclassifier. The waveform classifications then may be stored as a set ofdata containing the details for individual active waveforms that arestored as waveform classification 164 in the system database 125. Thefull ensemble waveform classifier 124 then adds to the knowledge and thedetails of the waveforms in the system database 125 as new energydetections, features, and potential waveforms are acquired.

In this example, the blind acquisition component 122 and the fullensemble waveform classifier 124 store and maintain the details aboutthe waveforms in the feature sets 160 and waveform classifications 164in the system database 125. The feature sets 160 are formed as groups offeatures that have been correlated, or found to be related and possiblya part of the same waveform, and, further as possibly a part of the samefrequency hopped signal. As an example, a feature may be stored with thefollowing example data structure:

class feature {    type    Kalman filter parameters    weight }

In this example, the feature type identifies the signal characteristicthat the feature represents, such as power, frequency, time, turn-ontime, turn-off time, or any other signal characteristic that may beutilized to identify a frequency hopped signal. Additionally, the Kalmanfilter parameters are parameters derived from performing a Kalman filterprocess on the energy detections during blind acquisition of the energyfeatures. The Kalman filtering is described below in more detail withreference to FIG. 3B.

The Kalman filter parameters may be configured with the following datastructure:

class Kalman Filter data {    state variables (state mean estimate,state co-variance)    PDF }

The state variables in the Kalman Filter parameters data structureinclude state mean estimates and state co-variances of a given feature,which are maintained and updated for the feature over time. In thisexample, the state mean estimate is the estimated mean of each state.The state co-variances are the co-variances of each state, where thestates are values maintained in accordance with the known Kalman filterprocess. The PDF is the probability density function for the feature,which includes the mean and standard deviation of the features. In thisexample, the mean may be extracted from the state mean estimate. Thestandard deviation may be determined separately from the Kalman filterstate variables to define the complete probability density function. ThePDF parameters may be defined in a data structure as follows:

class PDF {    mean;    standard deviation; }

In this example, as the features are acquired and processed by themaximum likelihood classifier, the features having a sufficientprobability of belonging to the same waveform, and in turn, the samefrequency hopped signal, are gathered in feature sets and stored in thefeature sets 160 data store as data groups. In general, a feature setmay be stored in the feature sets 160 data store as an array, or othergrouped or indexed data structure. As such, a feature set becomes apotential waveform due to the measure of their likelihood of belongingto the same signal. It is noted that the features in the feature setneed not belong to the same feature type that provide a correspondencewith the various characteristics that define a signal. As such, afeature set is one of the parameters in the data structure of thewaveform classification, which may be defined with the following datastructure:

class waveform classification {    feature set[ ]    signal type }

In this example, the above data structure defines a basic waveformclassification. As the waveform classifications are created fromfeatures acquired as energy detections are acquired over time, an activewaveform classification data structure may be defined with addedparameters. An active waveform classification may be defined to includeassigned energy detections and the waveform weight value. The weightvalue is generated as an average of the weights in the active featureset. That is, as noted above, each feature in the feature set has aweight parameter. The weight parameter of each of the features in thefeature set is determined by defining an averaged waveform to generatethe weight of the active waveform classification. As an example, anactive waveform classification may be defined with the following datastructure:

class active waveform {    feature set[ ],    assigned energydetections,    signal type,    weight }

In this example, the assigned detections in the active waveformclassification data structure includes energy detections that have beendetermined by the Maximum Likelihood Test to be likely to belongtogether as part of the waveform. The signal type may be one of anysignal type to which the waveform may belong. Example signal typesinclude FHSS, Non-Hopped Signal, Radar, etc. As noted above, the weightin the active waveform classification data structure is the average ofthe weights of the features in the feature set. Each feature has aweight associated with how well that feature is known and correlated.The waveform weight is a measure of how correlated and known all theactive features are.

The above defined data structures describe how the measurement ofvarious parameters associated with an energy detection, such as, power,frequency, bandwidth, turn-on time, turn-off time, etc. may be processedand used to learn, with machine learning, about the details of awaveform defining an RF signal. In general, energy detections areprocessed by the blind acquisition component 122. The blind acquisitioncomponent 122 identifies energy detection feature sets 160 as correlatedby a maximum likelihood determination that they belong to the samewaveform. The full ensemble waveform classifier 124 also determines thelikelihood that energy detections, are correlated, except that thedetermination is made with knowledge derived from all the learned activefeatures and the PDF of the features.

As the details of a waveform are learned, the knowledge is utilized toprocess energy detections using the acquired knowledge as a prioriknowledge. The directed re-acquisition component 126 receives energydetections and determines the likelihood that the energy detectionsbelong to known waveform classifications. The directed re-acquisitioncomponent 126 may also utilize a maximum likelihood classifier todetermine the likelihood using data in the feature sets 160 and in thewaveform classifications 164 stored in the database 125. The directedre-acquisition component 126 may however receive manually enteredwaveform classifications described in further detail with reference toFIG. 2. Additionally, manually entered waveform classifications also maybe entered by a user through a user interface, or directly through anI/O connection that may include data network connections.

In this example, the signal identification system 120 generates andmaintains waveform classifications including details about thewaveforms, particularly for waveforms defining frequency hopped signals.The waveform classifications may be generated in a report by a reportgenerator 166. The report generator 166 may output the waveformclassifications and frequency hopped signal details on the display 136.

As noted above, maximum likelihood classifiers are used in performingfunctions for the blind acquisition component 122, the full ensemblewaveform classifier 124, and the directed re-acquisition component 126.In this example, various maximum likelihood classifiers may be selectedfor use by each component.

Turning to FIG. 1B, FIG. 1B is a block diagram illustrating utilizationof a maximum likelihood classifier 170 in acquiring feature sets andwaveform classifications. The maximum likelihood classifier 170, in FIG.1B, may be configured as a weak classifier in blindly acquiring featuresets from the energy detections as described in more detail below withreference to FIGS. 3A and 3B. In general, the maximum likelihoodclassifier 170 receives energy detections, x, (which may be processed byensemble learning algorithms that detect various features that may beparticularly relevant to frequency hopped signals) as input, and mayalso receive features of a classifier c and related feature statistics,which may include the PDF for one or more features, Kalman filter statesfor one or more features, weights, co-variances, and other suitablestatistics as required by the selected maximum likelihood classifier. Inthis example, the output of the maximum likelihood classifier 170 is aprobability (Pr) that the feature set associated with the energydetection, x, belongs in the active waveform being tested.

In this example, the maximum likelihood classifier 170 may be configuredfor use as the full ensemble waveform classifier 124 (shown in FIG. 1A)by providing feature sets or potential waveforms as an input in additionto an input of the energy detections. Additionally, in this example, thefeatures of classifiers and feature statistics includes a larger set offeatures, v, and feature statistics that are based on the expandedlearning about the waveform classifications over time.

As noted above, any suitable maximum likelihood classifier may beutilized. In one example of an implementation, the maximum likelihoodclassifier 170 may be implemented utilizing a cumulative distributionfunction (“CDF”) that may be constructed over time using observedfeatures in the energy detects collected over time. In this example, thefeatures of the energy detections being tested are utilized to produce apoint on the CDF curve. The point on the CDF curve is a Pr having avalue between 0 and 1. In this example, the mean and standard deviationsused to produce the CDF, may be obtained from Kalman filtering asdescribed below with reference to FIGS. 3A and 3B.

In another example of an implementation, the maximum likelihoodclassifier 170, which may be implemented for either the blindacquisition component 122, the full ensemble waveform classifier 124, orthe directed re-acquisition component 126, may be a Gaussian NaïveBayesian Maximum Likelihood Classifier. Furthermore, other classifiersmay be implemented in other examples that include, without limitation:

1. Multivariate (Joint) Bayes Classifier;

2. Mahalanobis or Fischer Distance;

3. Bayesian Decision Tree;

4. Neural Networks;

5. Principal Component Analysis; and

6. Support Vector Machines.

In general, as additional energy detections are acquired, additionalfeatures and feature sets are acquired so that additional waveformclassifications are identified. As an example, for active waveforms, orwaveforms of signals still in the process of communicating data, thefeatures, feature sets 160, and waveform classifications 164 aresufficiently robust that energy detections may be directly correlatedwith waveform classifications 164 to refine the knowledge accumulatedfor active waveforms. In some example implementations, additionalinformation may be included in the waveform classifications 164 to allowfor more efficient correlation of additional energy detections. Thedirected re-acquisition component 126 may perform similar maximumlikelihood classifications of new energy detections as well as newfeatures and feature sets. Additionally, the directed re-acquisitioncomponent 126 may also include a user-input or model waveformclassifications to re-acquire energy detections. In an example of animplementation, waveform classifications may be generated and correlatedwith existing waveform classifications 164 to refine the knowledge aboutexisting waveform classifications 164, or about newly generated waveformclassifications.

FIG. 1C is a flowchart illustrating the operation of an example of animplementation of a method for acquiring a frequency hopped signalwithout prior knowledge about the signal. The method depicted in theflowchart in FIG. 1C may be performed using, for example, the system 100in FIG. 1A, although such a system 100 may take other forms. The processbegins and, at step 180, an RF signal is received at a RF receiver. TheRF receiver may include a single antenna, or multiple antennas arrangedin an array to allow for a determination of direction of arrival. Instep 182, the RF signal is converted into a stream of digital signallevels, preferably, as I,Q signal levels as is known in the art. At step184, energy detections are identified in the stream of digital signallevels. In this example, step 184 may include performing a predeterminednumber of FFTs during a selected measurement period. For example, theenergy detector 108, FIG. 1A, may perform 1000 FFTs on the stream ofdigital signal levels in a period of one second. A noise floor isestimated from the FFTs and each FFT time slot is tested against thenoise floor estimate to determine if that FFT time slot may be apotential hop in a frequency hopped signal. Then each potential hop isidentified as an energy detection for further testing.

In systems having an N-antenna array, step 184 may also include stepsof:

-   -   receiving RF signals at an N-antenna array, and    -   pre-filtering the RF signals received at the N antennas based on        direction of arrival.        The process of blindly acquiring features may include using a        direction acquisition detector to detect a waveform direction of        arrival, which may correlate direction of arrival features with        other features in a feature set.

In step 186, the energy detections are analyzed to identify a featureset that may describe a waveform for a frequency hopped signal. Theanalysis of the energy detections may be performed by acquiring shape,frequency, time, and direction (if available) features and analyzing thepotential features using corresponding weak classifiers. At step 188, atleast one waveform classification is generated from the feature set orsets identified in step 186. In step 188, additional feature statisticsmay be determined based on prior generated waveform classifications indetermining if the potential waveform or feature set from step 186belongs to a waveform classification. As energy detections are received,the step of generating waveform classifications involves storing thewaveform classification information and continuously updating theinformation. At step 190, energy detections are tested utilizing directre-acquisitioning based on a joint likelihood determined from allwaveform classification knowledge that is available that also includesmanually entered waveform classifications. In step 192, the generatedwaveform classifications are reported to a user of the system on adisplay, or other user interface. As the waveform classifications arerefined, their classification as a frequency hopped signal may beupdated and reported as well.

Turning to FIG. 2, a block diagram is shown of an example of a signalidentification system 200 that may be used in an example system forblindly acquiring a frequency hopped signal. FIG. 2 illustrates thesignal identification system 200 implemented in two basic tiers: a blindacquisition tier 202 and a waveform classification tier 204. The signalidentification system 200 incorporates the idea that initially, no priorknowledge is available to process the energy detections, but asadditional energy detections are acquired, additional features andfeatures sets are acquired to generate more waveform classificationsand/or more knowledge about identified waveform classifications.

In the signal identification system 200, the blind acquisition tier 202includes a shape weak classifier 210, a frequency weak classifier 212, atime weak classifier 214, and a direction weak classifier 216. The inputof energy detections is indicated by energy detections 217 with inputsto a lookback buffer 232 and an energy detect maximum likelihoodassignment test 230 in the waveform classifier tier 204. The energydetections are tested as discussed below using functions of the waveformclassifier tier 204 and unassigned energy detections at 201 are input atthe blind acquisition tier 202. It is noted that the signalidentification system 200 illustrates the processing of energydetections in the context of having prior knowledge of the waveformacquired from continuously processing energy detections in the receivedRF signal. The energy detections 217 are tested against this knowledgebefore processing without prior knowledge of the signal details.

In the blind acquisition tier 202, unassigned energy detections areprocessed by the shape weak classifier 210, the frequency weakclassifier 212, the time weak classifier 214, and the direction weakclassifier 216. Each weak classifier (210-216) processes the energydetections to identify features in the energy detections that maycorrespond to features of a waveform for a frequency hopped signal. Eachis a weak classifier (210-216) in that the likelihood of belonging to awaveform, or at this stage, a feature set, or a potential waveform, isdetermined using feature sets as opposed to using more developedwaveform classifications as is done in the waveform classifier tier 204.In this example, each weak classifier (210-216) includes a featuredetection process to identify features corresponding to particularsignal characteristics of a frequency hopped signal.

Turning to FIGS. 3A and 3B, these figures show examples of weakclassifiers that may be implemented in the signal identification system200 shown in FIG. 2. Referring to FIG. 3A, energy detections 302 areinput for the shape weak classifier 210, the frequency weak classifier212, the time weak classifier 214, and the direction weak classifier216. Each weak classifier identifies features that may belong to afeature set by analyzing features of multiple energy detections todetermine if they are correlated by maximum likelihood. The analysis ofthe energy detections then utilizes a positive correlation result toindicate that the energy detections are potential hops in a frequencyhopped signal. The feature is then marked as active and added to thefeature set. The maximum likelihood analysis determines the possibilitythat the energy detects are similar by one or more features.

The shape weak classifier 210 includes a shape acquisition detector 304and a shape weak maximum likelihood classifier 312. The shapeacquisition detector 304 analyzes the energy detections 302 to measuresignal characteristics corresponding to waveform shape characteristics,such as, for example, the signal bandwidth, dwell and absolute power ofthe energy detections. In FIG. 4, an example is shown that illustrateshow energy detections are analyzed by the shape acquisition detector 304to acquire shape features from the energy detections. As noted above,each energy detection 302 is a portion of a signal at a time slot in aperiod of measurements. Each energy detection 302 may be characterizedby the signal power, bandwidth, center frequency, turn-on time, andturn-off time at one of a number of FFTs performed over a period oftime. FIG. 4 shows a collection of energy detections 400 in a frequencyhopper format in which frequency is plotted along a vertical axis andtime is plotted along a horizontal axis. The shape acquisition detector304 (in FIG. 3A) may determine bandwidth, dwell, and absolute power ofeach energy detection and identify a set of possible hops 402 for afrequency hopped signal. The determined parameters (bandwidth, dwell,and absolute power) define the shape of each energy detection, whichappears to be the same for each of the possible hops 402.

Referring back to FIG. 3A, the set of possible hops 402 in FIG. 4 arethen processed by the shape weak maximum likelihood classifier 312 todetermine the likelihood of belonging to the same feature set. The shapeweak maximum likelihood classifier 312 may perform Kalman filtering totrack feature values to their true estimates. This process is describedin more detail with reference to FIG. 3B.

The frequency weak classifier 212 includes a frequency acquisitiondetector 306 and a frequency weak maximum likelihood classifier 314. Thefrequency acquisition detector 306 analyzes the energy detections 302 todetermine waveform frequency characteristics of the energy detections.Turning to FIG. 5, FIG. 5 illustrates how energy detections may beanalyzed by the frequency acquisition detector 306 to acquire waveformfrequency characteristics from the energy detections. In FIG. 5, acollection of energy detections 500 in a frequency hopper format isshown. The frequency acquisition detector 306 (in FIG. 3A) may analyzewaveform frequency characteristics to determine a signal centerfrequency, a frequency, a total bandwidth, and channel spacing of eachenergy detection and identify additional features for correlating a setof possible hops 502.

Referring back to FIG. 3A, the set of possible hops 502 in FIG. 5 areprocessed by the frequency weak maximum likelihood classifier 314 todetermine the likelihood of belonging to the same feature set. Thefrequency weak maximum likelihood classifier 314 may perform Kalmanfiltering to track feature values to their true estimates.

The time weak classifier 214 includes a time acquisition detector 308and a time weak maximum likelihood classifier 316. The time acquisitiondetector 308 analyzes the energy detections 302 to determine waveformtime characteristics of the potential frequency hopped signal. Turing toFIG. 6, FIG. 6 illustrates how energy detections may be analyzed by thetime acquisition detector 308 to acquire time or phase related featuresfrom the energy detections. FIG. 6 also shows a collection of energydetections 600 in a frequency hopper format. The time acquisitiondetector 308 (in FIG. 3A) may analyze time or phase characteristics toacquire a signal based on the periodic repetition of the energydetections and their relative time offset (or phase). Features acquiredby the time acquisition detector 308 include the rising edge time offset(phase), rising edge period, falling edge time offset (phase), andfalling edge period. Energy detections with similar time or phasecharacteristics are determined to be possible hops 602.

Referring back to FIG. 3A, the set of possible hops 602 in FIG. 6 areprocessed by the time weak maximum likelihood classifier 316 todetermine the likelihood of belonging to the same feature set. The timeweak maximum likelihood classifier 316 may perform Kalman filtering totrack feature values to their true estimates.

It is noted that each weak classifier may include specialized tests forfurther expanding the information available about the feature sets. Thetime weak maximum likelihood classifier 316 in FIG. 3A includes asequence arbiter 316 a and a sequence history 316 b. In general, thesequence arbiter 316 a and sequence history 316 b track details if thesequences and store the sequences as having:

1. Sequence number;

2. Likelihood; and

3. Highest likelihood energy detection identifier.

The sequence arbiter 316 a identifies the best features from thepossible hops as those belonging to a possible sequence of hops. Forexample, hops may be found to appear in the same time slot at the samefrequency. The sequence history 316 b may be utilized to correlate hopsappearing in sequences. The sequence arbiter 316 a and sequence history316 b tests may provide information about the sequence that a signaluses to switch frequencies in each time slot.

The direction weak classifier 216 includes a direction acquisitiondetector 310 and a direction weak maximum likelihood classifier 318.Turning to FIG. 7, FIG. 7 illustrates how energy detections may beanalyzed by the direction acquisition detector 310 to determine adirection of arrival for the energy detections. In a receiver (such asRF receiver 102 in FIG. 1A) having multiple antennas (N-antennas) in anarray formed so that each antenna focuses on signals from a specificdirection, the signals from each antenna may be compared to determinewhich antenna receives the strongest signal. The direction acquisitiondetector 310 may determine a direction of travel for each energydetection and identify those energy detections received from the samedirection. In FIG. 7, some energy detections 700 are identified asreceived from a direction of 270 degrees and set of energy detectionsreceived from a direction of 45 degrees. The direction of arrival may beidentified as a feature for a set of possible hops 702.

Referring back to FIG. 3A, the set of possible hops 702 in FIG. 7 areprocessed by the direction weak maximum likelihood classifier 318 todetermine the likelihood of belonging to the same feature set. Thefrequency weak maximum likelihood classifier 314 may perform Kalmanfiltering to track feature values to their true estimates.

The shape weak maximum likelihood classifier 312, the frequency weakmaximum likelihood classifier 314, the time weak maximum likelihoodclassifier 316 and the direction weak classifier 318 receive featuresthat are possibly related and determines the extent to which they arerelated. Referring to FIG. 3A, a weak maximum likelihood classifier 340is illustrated as representative of the shape weak maximum likelihoodclassifier 312, the frequency weak maximum likelihood classifier 314,the time weak maximum likelihood classifier 316 and the direction weakclassifier 318. The weak maximum likelihood classifier 340 receivespotential hops as input to a series of Kalman filter processes, Kalmanfilter time step 350 and Kalman filter predict 352, which generate amean and standard deviation of the features as state variables. Thestate variables are used to compute a likelihood at 354 that thefeatures are related generating a Pr as indicating the likelihood. ThePr is tested against a threshold probability of likelihood at alikelihood test 356. The likelihood test 356 may use information fromany specialized tests 358 that may be performed for a given weakclassifier, such as a sequence arbiter 358 a and a sequence history 358b.

If the likelihood test 356 determines that the likelihood, Pr, issufficiently high, a Kalman filter update 360 is performed as is typicalin the Kalman filter process. State variables are updated with the newinformation from the added features. Following the Kalman filter update,the feature weights for the features in the feature sets are computed at362. The weights are utilized to determine if the features should form afeature set as a potential waveform at 364.

Referring back to FIG. 2, the blind acquisition tier 202 generatespotential waveforms 203, or feature sets, for use in a full ensemblewaveform classification by the waveform classifier tier 204. Thepotential waveforms 203 are processed in a waveform tracking process 220to determine if the potential waveforms likely belong to one of a numberN of active waveforms 222. For each waveform N 222, an energy detectlikelihood test 228, a feature learning process 226, and a waveformclassification and error detection process 224 is performed. Thewaveform tracking process 220 also includes an energy detection maximumlikelihood assignment test 230, and a periodic updates process 234. Theenergy detections 206 may be received via a lookback buffer 232 asdescribed below.

The energy detect likelihood test 228 computes the likelihood that anunassigned energy detection belongs to a particular waveform N 222. Theunassigned energy detection may be received and tested to determine ifit belongs to known waveform classifications and if not, the energydetection remains unassigned and processed by the blind acquisition tier202. The energy detect likelihood test 228 utilizes the acquired featureknowledge of the waveform N 222 and information learned from the featurelearning process 226 to compute a probability that the energy detectbelongs to the waveform N 222. In an example of an implementation, amean and standard deviation of a feature is used to produce a GaussianCDF to produce a likelihood value.

The feature learning process 226 acquires feature knowledge, which mayinvolve learning the mean, standard deviation of the features over time.The feature learning process 226 receives features from energydetections determined to belong to the waveform N 222 by the energydetect likelihood test 228. The feature learning process 226 may use theKalman Filter updates to state variables that represent the features ofinterest. For certain specific features, such as frequency, featurelearning may also include learning details such as channel bandwidth,spacing, and frequency usage, which may be learned via histograms. Ingeneral, feature learning 226 takes a new energy detection and utilizesthe feature information provided to increase the feature knowledge ofthe waveform.

The waveform classification and error detection process 224 checksfeature sets or potential waveforms generated by the blind acquisitiontier 202 against the waveform classifications to determine if thefeature set matches a frequency hopped signal type. Once the feature setstatistics have converged enough to match a waveform classification, thewaveform classification is updated with the information from the featureset. An identification of the waveform classification may also bereported to a user via the user interface. As described below, theenergy detect maximum likelihood assignment test 230 determines if newenergy detections are likely to belong to one of the waveforms N 222already defined in the waveform classifications. The waveformclassification and error detection process 224 may also check that thefeature sets in the set of energy detections tracked by the energydetect maximum likelihood assignment test 230 still matches the originaltasking guidelines. If a waveform sufficiently deviates from theoriginal tasking guidelines, the waveform may be aborted or a newtasking may be generated.

The energy detection maximum likelihood assignment test 230 passes theenergy detections to the energy detect likelihood test 228, which passesback the combined likelihood and weight for all N waveforms. The energydetection maximum likelihood assignment test 230 then decides if one ofthe waveforms should accept the energy detection and chooses the bestwaveform. If a waveform is not chosen, the energy detection iscommunicated to the blind acquisition tier (tier 1) 202. In deciding ona waveform, the energy detection maximum likelihood assignment test 230takes the multiple active features in the waveforms and their respectivelikelihoods (i.e., Pr) and combines them into a single likelihood. Inone example, the respective Pr's are multiplied together, according toL(x|c), where x is a feature and c is a weak or full classifier. Thisfinal combined likelihood is then compared against a sigma multiplethreshold for inclusion. The threshold is the probability of x given amean of 0 with standard deviation of 1, where x can be 1 or more.Nominally, the system uses a 3 sigma threshold.

The lookback buffer 232 stores a series of energy detectionsrepresenting a signal received over a predetermined period of time. Inthis way, the loopback buffer 232 provides a way of going back in timeto process any missed energy detections using the best availablewaveform information while it is learned. The lookback buffer 232 issized to an approximate worst case missed hop, blind acquisition time ofa frequency hopped signal and also limited by the memory capacity of thesystem. While in most cases blind acquisition does not miss many hops,certain environments and hop rates may be more difficult.

If the energy detections 206 do not correlate with any existing activewaveforms 222, the energy detections 206 may be communicated to adirected re-acquisition component 240. The directed re-acquisitioncomponent 240 tests each unassigned energy detection to determine if itbelongs to any active potential waveforms within 240, which may includemanually entered waveform classifications 260. A user may enter waveformclassifications that specify a known frequency hopped signal. Themanually entered waveform classifications 260 may be generated andstored using a similar data structure as the learned waveformclassification 222. The directed re-acquisition component 240 usesfeature statistics relating to past learned waveform classifications 222or from the manually entered waveform classifications 260, as inputs tothe known features joint classifier 242. The feature statistics for thejoint classifier 242 are used to compute a likelihood of the unassignedenergy detect belonging to any of the waveform classifications from pastactive waveforms from 220 or manually input waveform classifications260. A joint likelihood test is performed to determine if thelikelihood, as a Pr, is sufficient to establish that the unassignedenergy detection is part of a potential waveform.

The directed re-acquisition component 240 reuses much of the same logicas that used in the blind acquisition tier 202 but with a filterspecifying the allowed feature value. In addition, the directedre-acquisition tier 240 may include a classification error detectioncomponent to evaluate if the initial classification decision wascorrect. If a waveform sufficiently deviates from the original taskingguidelines, the waveform may be aborted or a new tasking may begenerated.

In an example implementation, the signal identification system 120(shown in FIG. 1A) may produce energy detection fragmentation due tomarginal signal power or multipath fading. Such fragmentation interfereswith the association of the energy detections with other whole andcorrect detections. Intelligent energy detect defragmentation is amethod that may be used to join fragmented detections for use in theassociation algorithm. Two methods are available, blind and directeddefragmentation. Blind defragmentation joins fragments by estimating thenominal distance between energy detections and joining those that fallsignificantly below that distance value. Directed defragmentation usesknown information about the incoming energy detections to estimate whichfragments belong together.

As noted above, the waveform tracking process 220 includes a periodicupdates process 234, which may format, or store information for reportsto be used by a report generator 250 to output information about thewaveforms to the user. The periodic updates process 234 may keep theinformation for the reports and receive updated information as detailsof the waveforms are learned. In the early stage of an acquisition, theinformation may relate to unassigned energy detections or may includeinformation relating to noise signals. The report generator 250 thenuses the information to output a report based on the informationreceived.

Typically, a report may be presented in a frequency hop map showingsignal hops plotted as frequencies versus time. Turning to FIG. 8A, FIG.8A is a screenshot of an example report 800 for a blind acquisition of afrequency hopped signal (FHSS). The process in progress as shown in FIG.8A may include the results of the analysis of energy detections by theblind acquisition component 122, the full ensemble waveform classifier124, and directed re-acquisition component 126 (all in FIG. 1A). Theresults of the analyses are plotted as hops 802 at frequency bandwidthsand time slots of each hop. The hops 802 in the report 800 in FIG. 8Ahave frequency bands centered at a frequencies ranging from about 97.5Mhz and about 102.5 Mhz, which may represent a zoomed view of theresults as the overall bandwidth of the possible measurements may rangemuch higher in example implementations. It is noted that the viewillustrated in the report 800 in FIG. 8A may represent features of afeature set for an active waveform before a full specification of thefeatures of the waveform can be made. As noted above, waveformclassifications are learned over time. After a sufficient period oftime, energy detections from a particular signal are consistentlyclassified as belonging to a specific active waveform classification.The acquisitions of these energy detections are not actually “blind”after the sufficient time period in that the acquisitions are acquiredbased on the learned features of the corresponding active waveformclassification. The learned features of the corresponding activewaveform classifications thus become a priori knowledge of the detailsof the signal.

In FIG. 8B, an example of a screenshot of a report 850 of a completedFHSS detection is shown. The FHSS waveform detection may be deemedcompleted when energy detections are acquired and classified based onthe learned features of an active waveform classification at the directre-acquisition component 126 (in FIG. 1A). It is noted that the activewaveform classification may be provided manually and stored as awaveform classification for which the details are known beforehand. Inthis example, the energy detections are acquired in a directed search bythe direct re-acquisition component 126 (in FIG. 1A).

When an FHSS detection is complete, additional reports may be generatedthat provide details about the signal. Much of the details about thesignal is stored as features and features sets stored as part of thewaveform classifications as described above. Other details may begenerated from the features and feature sets. An example FHSSCharacteristics Output Report may provide the following details:

1. Waveform ID: Blind Hopped 500 HPS;

2. Waveform Seed: Phase Weak Classifier;

3. Waveform Signal Class: FREQUENCY_HOPPER;

4. Estimated Burst Size: 2000;

5. Associated Energy Detects: 1916;

6. Phase Estimate: 1.43639e+09 sec;

7. Period Estimate: 500.238 μsec;

8. Num Hits: 1916;

9. Num Misses: 84;

10. Power Estimate: 22.8259 dB;

11. Dwell Estimate: 422.741 μsec;

12. Width Estimate: 16194.9 Hz;

13. Center Freq Estimate: 199.941 MHz;

14. Centroid Freq Estimate: 199.647 MHz;

15. Bandwidth Estimate: 4.88672 MHz; and

16. DOA: 298 degrees.

Other types of information may be provided as well for other signaltypes, or in other implementations.

The above-described examples of implementations further include thefollowing example implementations indicating manners in which variationsof embodiments of the disclosure may arise. In one aspect of generatingat least one waveform classification, the method includes: subsequentlyblindly acquiring features from the energy detections in the stream ofdigital signals; and performing ensemble learning comprising a pluralityof learning methods to acquire features for generating the waveformclassification.

In another aspect, the step of performing ensemble learning comprises,where the plurality of learning methods includes a phase acquisitionmethod, acquiring an off-time period, an on-time period, an on-timephase and an off-time phase for energy detections corresponding tofeature sets where the waveform classification indicates the signal is afrequency hopped sequence.

In another aspect, the step of performing ensemble learning includes:(where the plurality of learning methods includes a sequence arbitermethod) tracking slots for a waveform having a waveform classificationindicating the signal is a frequency hopped sequence; testing theidentified energy detections to determine a probability of belonging tothe frequency hopped sequence; and selecting the energy detection withthe highest probability of belonging as part of the waveform.

In another aspect, the step of performing ensemble learning includes:(where the plurality of learning methods includes a sequence historymethod) storing the energy detection with the highest probability ofbelonging in a sequence history of slots in the waveform; storing theprobability determined for the energy detection; and storing a sequencetime indicating a time at which the energy detection was determined tobe the highest probability energy detection.

In another aspect, the method may include steps of reporting thewaveform classification and features set of the waveform classificationby output to a user interface device.

Example implementations of a system for acquiring frequency hoppedsignals also may include the following examples. In one aspect, anexample of an implementation of a system having a non-transitorycomputer-readable medium stores executable instructions for a fullensemble waveform classifier that, when executed by the processor, areoperative to: subsequently blindly acquire features from the energydetections in the stream of digital signals; and correlate acquiredfeatures and the feature set using a maximum likelihood classifier.

In another aspect, the non-transitory computer-readable medium may storeexecutable instructions for the full ensemble waveform classifier that,when executed by the processor, are operative to: subsequently blindlyacquire features from the energy detections in the stream of digitalsignals; and perform ensemble learning comprising a plurality oflearning methods to acquire features for generating the waveformclassification.

In another example, the step of performing ensemble learning may include(where the plurality of learning methods includes a phase acquisitionmethod) acquiring an off-time period, an on-time period, an on-timephase and an off-time phase for energy detections corresponding tofeature sets where the waveform classification indicates the signal is afrequency hopped sequence.

In another aspect, the step of performing ensemble learning may include:(where the plurality of learning methods includes a sequence arbitermethod) tracking slots for a waveform having a waveform classificationindicating the signal is a frequency hopped sequence; testing theidentified energy detections to determine a probability of belonging tothe frequency hopped sequence; and selecting the energy detection withthe highest probability of belonging as part of the waveform.

In another aspect, the step of performing ensemble learning may include(where the plurality of learning methods includes a sequence historymethod) storing the energy detection with the highest probability ofbelonging in a sequence history of slots in the waveform; storing theprobability determined for the energy detection; and storing a sequencetime indicating a time at which the energy detection was determined tobe the highest probability energy detection.

In another aspect, an example system may further include a userinterface device; and a waveform classification report generatorconfigured to generate a report indicating the waveform classificationand feature set of the wave form classification for output to the userinterface device.

It will be understood that various aspects or details of the disclosuremay be changed without departing from the scope of the disclosure. Theabove description is not exhaustive and does not limit the claimeddisclosures to the precise form disclosed. Furthermore, the abovedescription is for the purpose of illustration only, and not for thepurpose of limitation. Modifications and variations are possible inlight of the above description or may be acquired from practicing thedisclosure. The claims and their equivalents define the scope of thedisclosure.

What is claimed is:
 1. A method for acquiring a frequency hopped signalwith no prior knowledge about the frequency hopped signal, the methodcomprising: receiving a radio frequency (RF) signal at a receiver;converting the RF signal into a stream of digital signal levels;identifying energy detections in the stream of digital signal levels aspossible hops of the frequency hopped signal; blindly acquiring afeature set for defining the frequency hopped signal from the energydetections by: acquiring shape features from the energy detections bydetermining signal bandwidth, dwell, and absolute power and performing ashape maximum likelihood classification to determine a likelihood thatthe energy detections belong to a same feature set without comparing toany predetermined parameters; acquiring frequency features from theenergy detections by determining a signal center frequency, a frequency,a total bandwidth, and channel spacing for each energy detection andperforming a frequency maximum likelihood classification to determine alikelihood that the energy detections belong to a same feature setwithout comparing to any predetermined parameters; and identifying atleast one feature set from the shape maximum likelihood classificationand from the frequency maximum likelihood classification; generating atleast one waveform classification based on the at least one feature set;directly acquiring energy detections from the RF signal based on thewaveform classification; and reporting the at least one waveformclassification as the frequency hopped signal.
 2. The method of claim 1,wherein the step of blindly acquiring the feature set includes measuringselected signal characteristics of the energy detections, identifyingfeatures from the measured signal characteristics, and determining alikelihood that identified features are correlated to form the featureset.
 3. The method of claim 2, wherein the features identified frommeasured signal characteristics are features selected from a groupconsisting of detect power, on-time period/phase, off-time period/phase,detect frequency and bandwidth, direction of arrival, and waveformfrequency use and bandwidth.
 4. The method of claim 1, wherein the stepof blindly acquiring the feature set comprises using a shape acquisitiondetector to measure signal characteristics corresponding to waveformshape characteristics.
 5. The method of claim 1, wherein the step ofblindly acquiring comprises detecting using a frequency acquisitiondetector to measure signal characteristics corresponding to waveformfrequency characteristics.
 6. The method of claim 1, wherein the step ofblindly acquiring the feature set comprises using a time acquisitiondetector to measure signal characteristics corresponding to waveformtime characteristics.
 7. The method of claim 1, further includingreceiving RF signals at an N-antenna array, and pre-filtering the RFsignals received at the N antennas based on direction of arrival,wherein the step of blindly acquiring comprises using a directionacquisition detector to detect a waveform direction of arrival.
 8. Themethod of claim 1, wherein the step of blindly acquiring the featuresset further includes determining a probability density function (“PDF”)for each feature, and utilizing the PDF of the feature to determine alikelihood that the feature set and other features are correlated. 9.The method of claim 8, further including utilizing a plurality of weakclassifiers to determine a likelihood that acquired features and thefeature set are correlated.
 10. The method of claim 9, wherein theplurality of weak classifiers utilize Kalman filtering to compute alikelihood that an acquired feature belongs to the feature set.
 11. Themethod of claim 1, wherein the step of generating the at least onewaveform classification includes subsequently blindly acquiring featuresfrom the energy detections in the stream of digital signals, andcorrelating acquired features and the feature set utilizing the shapemaximum likelihood classifier and the frequency maximum likelihoodclassifier.
 12. A system for acquiring a frequency hopped signal with noprior knowledge about the frequency hopped signal, the systemcomprising: a receiver including at least one antenna for receiving anRF signal; a digitizer, wherein the digitizer is configured to performanalog to digital conversion of the RF signal and as a result, convertthe RF signal to a digital signal stream; an energy detector, whereinthe energy detector is configured to identify energy detections in thedigital signal stream; a processor; and a non-transitorycomputer-readable medium storing processor-executable instructions thatwhen executed by the processor are configured to perform functions for ablind acquisition component configured to blindly acquire a feature setto define a frequency hopped signal from the energy detections by:acquiring shape features from the energy detections by determiningsignal bandwidth, dwell, and absolute power and performing a shapemaximum likelihood classification to determine a likelihood that theenergy detections belong to a same feature set without comparing to anypredetermined parameters, acquiring frequency features from the energydetections by determining a signal center frequency, a frequency, atotal bandwidth, and channel spacing for each energy detection andperforming a frequency maximum likelihood classification to determine alikelihood that the energy detections belong to a same feature setwithout comparing to any predetermined parameters, and identifying atleast one feature set from the shape maximum likelihood classificationand from the frequency maximum likelihood classification, a fullensemble waveform classifier that is configured to generate at least onewaveform classification based on the feature set, a directedre-acquisition component configured to acquire energy detections basedon the waveform classification, and a report generator that isconfigured to report the at least one waveform classification as afrequency hopped signal.
 13. The system of claim 12, wherein thenon-transitory computer-readable medium stores executable instructionsfor the blind acquisition component that, when executed by theprocessor, are configured to measure selected signal characteristics ofthe energy detections, identify features from the measured signalcharacteristics, and determine a likelihood that identified features arecorrelated to form the feature set.
 14. The system of claim 12, whereinthe features identified from measured signal characteristics arefeatures selected from a group consisting of detect power, on-timeperiod/phase, off-time period/phase, detect frequency and bandwidth,direction of arrival, and waveform frequency use and bandwidth.
 15. Thesystem of claim 12, wherein the non-transitory computer-readable mediumstores executable instructions for the blind acquisition component that,when executed by the processor, are operative to perform functions for ashape acquisition detector to measure signal characteristicscorresponding to waveform shape characteristics.
 16. The system of claim12, wherein the non-transitory computer-readable medium storesexecutable instructions for the blind acquisition component that, whenexecuted by the processor, are operative to perform functions for a timeacquisition detector to measure signal characteristics corresponding towaveform time characteristics.
 17. The system of claim 12, furtherincluding an N-antenna array, wherein the N-antenna array is configuredto receive the RF signal and to pre-filter the RF signal received at theN antennas based on direction of arrival, wherein the non-transitorycomputer-readable medium stores executable instructions for the blindacquisition component that, when executed by the processor, areoperative to perform functions for a direction acquisition detector todetect a waveform direction of arrival.
 18. The system of claim 12,wherein the non-transitory computer-readable medium stores executableinstructions for the blind acquisition component that, when executed bythe processor, are configured to subsequently blindly acquire featuresfrom the energy detections in the stream of digital signals, determine aprobability density function (“PDF”) for each feature, and utilize thePDF of the feature to determine a likelihood that the feature set andsubsequently acquired features are correlated.
 19. The system of claim18, further including a plurality of weak classifiers, where theplurality of weak classifiers are configured to determine a likelihoodthat acquired features and the feature set are correlated.
 20. Thesystem of claim 19, wherein the plurality of weak classifiers utilizeKalman filtering to compute a likelihood that an acquired featurebelongs to the feature set.